In some conventional receivers, improvements in performance may require extensive system modifications that may be very costly and, in some cases, may even be impractical. Determining the right approach to achieve design improvements may depend on the optimization of a receiver system to a particular modulation type and/or to the various kinds of noises that may be introduced by a transmission channel. For example, the optimization of a receiver system may be based on whether the signals being received, generally in the form of successive symbols or information bits, are interdependent. Signals received from, for example, a convolutional encoder, may be interdependent signals, that is, signals with memory. In this regard, a convolutional encoder may generate non-return-to-zero inverted (NRZI) or continuous-phase modulation (CPM), which is generally based on a finite state machine operation.
One method or algorithm for signal detection in a receiver system that decodes convolutional encoded data is maximum-likelihood sequence detection or estimation (MLSE). The MLSE is an algorithm that performs soft decisions while searching for a sequence that minimizes a distance metric in a trellis that characterizes the memory or interdependence of the transmitted signal. In this regard, an operation based on the Viterbi algorithm may be utilized to reduce the number of sequences in the trellis search when new signals are received.
Another method or algorithm for signal detection of convolutional encoded data that makes symbol-by-symbol decisions is maximum a posteriori probability (MAP). The optimization of the MAP algorithm is based on minimizing the probability of a symbol error. In many instances, the MAP algorithm may be difficult to implement because of its computational complexity.
Another historical approach to improve the performance of receivers that may require extensive system modifications is to reduce the effect of interference by using multiple antennas, often referred to as receive or antenna diversity. The benefits of diversity increase with the number of antennas that may be used. Moreover, the more uncorrelated that the antennas are to each other through appropriate spacing, the better the performance improvement of the system. However, receiving signals from multiple antennas increases hardware and/or software complexity resulting in higher implementation costs.
Improvements in the design and implementation of optimized receivers for decoding convolutional encoded data may require modifications to the application of the MLSE algorithm, the Viterbi algorithm, and/or the MAP algorithm in accordance with the modulation method utilized in signal transmission. Moreover, optimized receivers may also need to implement techniques that enable the reduction of signal interference without extensive and costly increases in hardware and/or software complexity.
Multilayer processing may be utilized to optimize portions of a receiver's processing more efficiently. However, when decoding improvements comprise a plurality of techniques, it may be difficult to construct an appropriate multilayer implementation that achieves the required design improvements with minimum hardware and/or software complexity.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such systems with some aspects of the present invention as set forth in the remainder of the present application with reference to the drawings.